Python Pandas Cheat Sheet: Master Data Analysis Fast!

Unlock the Power of Pandas with Our Ready-to-Use Python Cheat Sheets

Struggling to remember Pandas commands or spend hours searching for solutions?
Our Python Pandas Cheat Sheet bundle gives you 7 expertly crafted Jupyter notebooks that serve as your quick-reference guide and hands-on tutorial for real-world data analysis.

What’s Inside?

• Data Analysis - Dates.ipynb
  Master working with dates & timestamps — extract, manipulate, calculate, and format with ease.
• Data Analysis - Group By.ipynb
  Learn how to group data and apply advanced aggregation functions like a pro.
• Data Analysis - Lambda and Masks.ipynb
  Use lambda functions and filters to perform complex data transformations effortlessly.
• Data Analysis - Plotting in Pandas.ipynb
  Create stunning charts and dashboards directly from your DataFrame.
• Pandas for Excel Developers.ipynb
  Seamlessly translate your Excel knowledge into powerful Pandas workflows.
• Pandas for SQL Developers.ipynb
  Perform SQL-like operations—joins, updates, vlookups—using Pandas.
• Data Analysis - Pivot Tables.ipynb
  Build pivot tables and apply conditional logic for deep data insights.

What You Will Learn (solve 60+ common Pandas tasks)

• How to get today's date with timestamp
• How to get today's date with NO timestamp
• How to get the timestamp of a date
• How to get the day of a date
• How to get the month of a date
• How to get the year of a date
• How to get yesterday's date
• How to get last month's date
• How to get the first day of last month
• How to get the last day of last month
• How to get the Monday of last week
• How to get the Sunday of last week
• Basic date math
• How to group by one column
• How to group by multiple columns
• How to iterate over a group
• How to apply built-in functions like sum and std
• How does group by work
• How to add a new column to a group
• How to sum a column but keep the same shape of the DataFrame
• How to perform multiple aggregations at the same time
• How to choose aggregation methods per column
• How to add custom labels to multiple aggregations
• Examples using lambda
• Which rows are greater than 10
• Comparisons with lambda
• Returning Boolean values
• Lambda with multiple inputs
• Comparing functions with lambda functions
• How to plot a line chart
• How to plot a bar chart
• How to label the legend
• How to create a legend
• How to label the x axis
• How to label the y axis
• How to give the chart a title
• How to create side-by-side charts
• How to create dashboards with multiple charts
• How to size your charts
• How to choose different colors and line styles
• How to add a column and sum horizontally
• How to add a column and compute the average
• How to add a column and compute the percentage of Total Sales
• How to sort by a column
• How to filter by a value
• How to create a column chart
• How to create a pivot table
• How to perform a vlookup
• How to perform an IF/THEN statement
• How to declare variables
• How to update variables
• How to update a table
• How to get current date, yesterday, last year
• How to get first of month or last day of month
• How to insert into a table from another table
• How to join two tables
• How to select n number of rows
• How to select rows in ascending/descending order
• How to select unique values (no duplicates)
• How to write a case statement within an update
• How to check for NULL values
• How to use the keyword "IN"
• How to count all of the rows in a table
• How to delete contents of a table
• How to select the smallest/largest value in a column
• How to string match
• How to organize a DataFrame by specific columns
• How to fill NaN values
• How to add sub-totals to the columns and rows
• How to use the sum function
• How to use the count function
• How to use the mean function
• How to use the max function
• How to use the min function
• How to use the len function
• How to apply different functions to different columns
• How to apply multiple functions to one column
• How to apply a custom function
• How to glue pivot tables together

…and so much more, all explained with hands-on examples you can run and modify immediately.

Why Choose This Pandas Cheat Sheet Bundle?

✅ Instant access to practical code examples
✅ Step-by-step tutorials for fast learning
✅ Designed for Excel & SQL users switching to Pandas
✅ Perfect for beginners and intermediate users alike
✅ Save hours of Googling & frustration

Ready to Master Pandas?

Grab your Python Pandas Cheat Sheet bundle now and transform your data analysis skills with clear, concise, and runnable Jupyter notebooks.